Figure 4: Impact of the proportion of injected noise for learning Paragraph Embeddings on XSum dataset. PPLint and the PPL of the generation obtained from training PLANNER on the corresponding z at different noise level. We observed when the value of p is within (0, 0.7), there Performing a grid search on each task using diffusion models is an expensive process. However, it has been observed that an increase in the value of p leads to a deviation between the two. This could be attributed to a higher conversion error that occurs when p is excessively large. A.2 Selection of number of latent code k The parameter k determines the number of latent codes used to represent a paragraph and therefore controls the compression level. Latent codes with smaller values of k are easier to model using the diffusion model, but may struggle to accurately preserve all the information in the original text. Additionally, smaller values of k offer computational efficiency as the sequence length for the diffusion model is k. To determine the best set of latent codes, we conducted experiments using three different methods: 1) selecting the first k hidden vectors, 2) selecting the last k hidden vectors, and 3) selecting interleaving hidden vectors, one for every L k hidden vectors. The results of the ablation study are presented in Table 5. Based on our findings, we observed no significant difference among the different choices, so we opted for option 1). Furthermore, we discovered that increasing the value of k does not lead to a dramatic improvement in performance. To balance between efficiency and performance, in most of our study we only use k =16 Setup BLEU_clean BLEU_robust First k (k=16) 79.59 43.17 A.3 Reconstruction, denoising and interpolation examples In Table 6, we present examples that demonstrate the adeptness of the trained Variational Paragraph Embedder in providing clean and denoised reconstructions. Additionally, we showcase interpolation results (Table 7, 8) derived from two random sentences in the hotel review dataset. The interpolated paragraph is usually coherent and incorporates inputs from both sentences, characterizing the distributional smoothness of the latent space. Reconstructed text complaints: after two nights stay, i asked the maid to clean our room (empty the wastebasket & make the bed). Denoising reconstruction (hotel review), noise level 0.3 Original text * * * check out the bathroom picture * * * i was in nyc by myself to watch some friends participate in the us olympic marathon trials. Corrupted text * * [unused697] check exams the bathroom picture * * slams i was in nyc mead myself yankee 2016 some scotch ruin in the outfielder olympicnca trials.

Figure 4: Impact of the proportion of injected noise for learning Paragraph Em-beddings on XSum dataset. (Figure 4). The results of the ablation study are presented in Table 5. Embedder in providing clean and denoised reconstructions. In general, it has been observed that generations progress in a coarse-to-fine manner. The early time step, which is close to 1, tends to be less fluent and generic. This was the nicest stay we have ever had. Turtle Bay was a great resort. This was the nicest stay we have ever had.

Chris Pratt on new film Mercy: I asked to be locked into an executioner's chair Being locked barefoot in an executioner's chair sounds uncomfortable, but that is what Chris Pratt requested for his latest film, Mercy. More familiar as a wisecracking action hero in blockbusters like Guardians of the Galaxy and Jurassic World, this role is quite a departure for him. He plays homicide detective Chris Raven, who's fighting for his life after being accused of murdering his wife. Raven is an alcoholic who wakes in the chair after a drinking binge, with just 90 minutes to convince an AI judge he's innocent, or he'll be executed immediately. The film is set in real time, so we see Raven defend his case - while enduring a crashing hangover.

The 2025 Oscar nominations were announced on Thursday, and two New Yorker films are among the contenders. "Incident," which uses body-camera and surveillance footage to examine a police shooting in Chicago, is nominated in the Documentary Short Film category, while "I'm Not a Robot," a darkly humorous Dutch film about a woman taking a series of CAPTCHA tests, is nominated for best Live Action Short. Seventeen previous New Yorker films have been nominated for Academy Awards; a victory at this year's ceremony, scheduled for March 2nd in Los Angeles, would be the magazine's first win. "Incident," directed by Bill Morrison, who produced with Jamie Kalven, chronicles a police killing and its aftermath. On a Chicago sidewalk, an African American man named Harith (Snoop) Augustus is questioned and then pursued by a foot patrol after leaving the barbershop where he works; after a brief scuffle, he is fatally wounded.

Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to exposure bias - the difference between how a model is trained, and how it is used during inference. Denoising diffusion models provide an alternative approach in which a model can revisit and revise its output. However, they can be computationally expensive and prior efforts on text have led to models that produce less fluent output compared to autoregressive models, especially for longer text and paragraphs. In this paper, we propose PLANNER, a model that combines latent semantic diffusion with autoregressive generation, to generate fluent text while exercising global control over paragraphs. The model achieves this by combining an autoregressive "decoding" module with a "planning" module that uses latent diffusion to generate semantic paragraph embeddings in a coarse-to-fine manner. The proposed method is evaluated on various conditional generation tasks, and results on semantic generation, text completion and summarization show its effectiveness in generating high-quality long-form text in an efficient manner.

Privid's a privacy-preserving video analytics system supports aggregation queries, which process large amounts of video data. "Privid" could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people. Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale -- but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. Security cameras can do a lot -- they've become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes "hero tool" in crime media.

Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. As these powerful little devices have cropped up seemingly everywhere, the use of machine learning tools has automated video content analysis at a massive scale -- but with increasing mass surveillance, there are currently no legally enforceable rules to limit privacy invasions. Security cameras can do a lot -- they've become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes "hero tool" in crime media. Now, video surveillance can help health officials measure the fraction of people wearing masks, enable transportation departments to monitor the density and flow of vehicles, bikes, and pedestrians, and provide businesses with a better understanding of shopping behaviors. But why has privacy remained a weak afterthought?

In January, Detroit police arrested and charged 42-year-old Robert Williams with stealing $4,000 in watches from a retail store 15 months earlier. Taken away in handcuffs in front of his two children, Williams was sent to an interrogation room where police presented him with their evidence: Facial recognition software matched his driver's license photo with surveillance footage from the night of the crime. Williams had an alibi, The New York Times reports, and immediately denied the charges. Police pointed to the image of the suspect from the night of the theft. "I just see a big black guy," he told NPR.

The FBI has failed to appease concerns about the use of its facial recognition technology in criminal investigations. Multiple issues were raised three years ago after a congressional watchdog urged the bureau to improve its practices in order to meet privacy and accuracy standards. The FBI - and other US law enforcement agencies - have been using the Next Generation Identification-Interstate Photo System since 2015. It uses facial recognition software to link potential suspects to crimes from a vast database of 30 million pictures, including mugshots. The report slamming the FBI for its failure to moderate the software comes as the bureau increases its use of the technology.

The FBI is trialing Amazon's controversial facial recognition tech, Rekognition, as a new method to catch criminals. It began working with the agency in early 2018, according to Nextgov. Rekognition is expected to help speed up the FBI's process of going through video surveillance footage that's collected during investigations. The FBI is trialing Amazon's controversial facial recognition tech, Rekognition, as a new method to catch criminals. The technology could prove to be especially helpful during time-sensitive counterterrorism investigations.